Optimizing Storage Capacity of Retailers in Stochastic Periodic Inventory Routing Problem

A challenging question in Stochastic Periodic Inventory Routing Problem (SPIRP) is how to deal with stochastic demand rates, while minimizing the costs (transportation, inventory, and storage) and finding the best routing system. In this paper, we reformulate the SPIRP model to a safety stock-based SPIRP where the inventory storage capacity at the retailers are considered as variables and retailer’s demand rate is stochastic. The supply chain planner needs to find the best routing system to replenish the retailers with the most optimum level of inventory, while the service level is satisfied in a long term planning horizon. Four different policies for storage capacity optimization are presented, evaluated, and compared in an illustrative example. The impact of storage capacity limitation is considered based on the defined policies to measure their compatibility for different situations.